Re-ranking search results using an additional retrieved list

Meister, Lior; Kurland, Oren; Kalmanovich, Inna Gelfer

doi:10.1007/s10791-010-9150-8

Cited by 15 publications

(18 citation statements)

References 53 publications

(97 reference statements)

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“…In a context different from personalization, namely that of methods for fusion of retrieved lists, Meister et al [34], [35] rerank a list retrieved in response to a query utilizing a second list. This list is retrieved by using a different retrieval method and/or query representation, and exploiting inter-document similarities between the lists, so as to improve precision in the very top ranks.…”

Section: General Personalization Methodsmentioning

confidence: 99%

“…This list is retrieved by using a different retrieval method and/or query representation, and exploiting inter-document similarities between the lists, so as to improve precision in the very top ranks. Their methods can be used in the context of blind feedback-based automatic query expansion by reranking the list produced by blind feedback using the list retrieved in response to the original query [34]. Similarly, Zighelnic and Kurland [51] fuse the results retrieved in response to the original query and to its expanded form.…”

Section: General Personalization Methodsmentioning

confidence: 99%

“…Then the obtained lists of results would be combined in some way. This approach may be seen as a particular kind of reranking but has a main drawback: the overlapping degree between the two lists of results would likely be very low (because the query terms and the profile terms may be quite unrelated), and therefore, their simple combination would be worthless [34]. This approach of combining or fusing two different lists is more useful within pseudo-relevance feedback techniques, where the query terms in the additional query come from the top documents retrieved by the original query.…”

Section: Rerankingmentioning

confidence: 99%

“…The opposite approach (i.e. reranking the results of the expanded query using the original query) has been considered within the field of pseudo-relevance feedback [34].…”

Section: Rerankingmentioning

confidence: 99%

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Using Personalization to Improve XML Retrieval

Campos

Fernández‐Luna

Huete

et al. 2014

IEEE Trans. Knowl. Data Eng.

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Abstract-As the amount of information increases every day and the users normally formulate short and ambiguous queries, personalized search techniques are becoming almost a must. Using the information about the user stored in a user profile, these techniques retrieve results that are closer to the user preferences. On the other hand, the information is being stored more and more in an semi-structured way, and XML has emerged as a standard for representing and exchanging this type of data. XML search allows a higher retrieval effectiveness, due to its ability to retrieve and to show the user specific parts of the documents instead of the full document. In this paper we propose several personalization techniques in the context of XML retrieval. We try to combine the different approaches where personalization may be applied: query reformulation, reranking of results and retrieval model modification. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach.

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Section: General Personalization Methodsmentioning

confidence: 99%

Section: General Personalization Methodsmentioning

confidence: 99%

Section: Rerankingmentioning

confidence: 99%

“…The opposite approach (i.e. reranking the results of the expanded query using the original query) has been considered within the field of pseudo-relevance feedback [34].…”

Section: Rerankingmentioning

confidence: 99%

See 2 more Smart Citations

Using Personalization to Improve XML Retrieval

Campos

Fernández‐Luna

Huete

et al. 2014

IEEE Trans. Knowl. Data Eng.

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show abstract

“…4.3, was not utilized (Krikon et al 2009). Re-ranking an initially retrieved list using inter-document similarities was also employed for searching over digital libraries (Van and Beigbeder 2008), cross-lingual retrieval (Diaz 2008), and fusion of retrieved lists (Meister et al 2010).…”

Section: Related Workmentioning

confidence: 99%

A study of the integration of passage-, document-, and cluster-based information for re-ranking search results

Krikon

Kurland

2011

Inf Retrieval

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Cluster-based and passage-based document retrieval paradigms were shown to be effective. While the former are based on utilizing query-related corpus context manifested in clusters of similar documents, the latter address the fact that a document can be relevant even if only a very small part of it contains query-pertaining information. Hence, cluster-based approaches could be viewed as based on ''expanding'' the document representation, while passage-based approaches can be thought of as utilizing a ''contracted'' document representation. We present a study of the relative benefits of using each of these two approaches, and of the potential merits of their integration. To that end, we devise two methods that integrate whole-document-based, cluster-based and passage-based information. The methods are applied for the re-ranking task, that is, re-ordering documents in an initially retrieved list so as to improve precision at the very top ranks. Extensive empirical evaluation attests to the potential merits of integrating these information types. Specifically, the resultant performance substantially transcends that of the initial ranking; and, is often better than that of a state-of-the-art pseudo-feedback-based query expansion approach.

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From “Identical” to “Similar”: Fusing Retrieved Lists Based on Inter-document Similarities

Kozorovitsky

Kurland

2009

Lecture Notes in Computer Science

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Methods for fusing document lists that were retrieved in response to a query often utilize the retrieval scores and/or ranks of documents in the lists. We present a novel fusion approach that is based on using, in addition, information induced from inter-document similarities. Specifically, our methods let similar documents from different lists provide relevance-status support to each other. We use a graph-based method to model relevancestatus propagation between documents. The propagation is governed by inter-documentsimilarities and by retrieval scores of documents in the lists. Empirical evaluation demonstrates the effectiveness of our methods in fusing TREC runs. The performance of our most effective methods transcends that of effective fusion methods that utilize only retrieval scores or ranks.1. We use the term "overlap" to refer to the number of documents shared by the retrieved lists rather than in reference to content overlap.

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Re-ranking search results using an additional retrieved list

Cited by 15 publications

References 53 publications

Using Personalization to Improve XML Retrieval

Using Personalization to Improve XML Retrieval

A study of the integration of passage-, document-, and cluster-based information for re-ranking search results

From “Identical” to “Similar”: Fusing Retrieved Lists Based on Inter-document Similarities

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